Overview

Dataset statistics

Number of variables50
Number of observations139
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory59.5 KiB
Average record size in memory438.0 B

Variable types

Categorical25
Numeric18
Unsupported7

Alerts

ano has constant value "2021"Constant
estado (sigla) has constant value "TO"Constant
Região has constant value "Norte"Constant
Dados de Identificação/Demográficos - População - Fonte has constant value "IBGE/2020"Constant
Área da Unidade Territorial - Fonte has constant value "IBGE/2010"Constant
Pib per capita 2020 - Fonte has constant value "IBGE/2020"Constant
Pib a preços correntes (R$ 1.000) - Fonte has constant value "IBGE/2020"Constant
Finanças - Investimento per Capita - Invest. Educação - Fonte has constant value "STN/2019"Constant
Finanças - Investimento per Capita - Invest. Saúde - Fonte has constant value "STN/2019"Constant
Finanças - Fiscal - Autonomia - Outlier has constant value "Não"Constant
Finanças - Fiscal - Autonomia - Fonte has constant value "Firjan/2019"Constant
Finanças - Fiscal - Gasto com Pessoal - Outlier has constant value "Não"Constant
Finanças - Fiscal - Gasto com Pessoal - Fonte has constant value "Firjan/2019"Constant
Finanças - Fiscal - Investimentos - Outlier has constant value "Não"Constant
Finanças - Fiscal - Investimentos - Fonte has constant value "Firjan/2019"Constant
Finanças - Fiscal - Liquidez - Outlier has constant value "Não"Constant
Finanças - Fiscal - Liquidez - Fonte has constant value "Firjan/2019"Constant
nome has a high cardinality: 139 distinct valuesHigh cardinality
Nome_UF has a high cardinality: 139 distinct valuesHigh cardinality
codigo_municipio is highly overall correlated with Código IBGEHigh correlation
Código IBGE is highly overall correlated with codigo_municipioHigh correlation
Dados de Identificação/Demográficos - População is highly overall correlated with capital (s/n) and 7 other fieldsHigh correlation
Área da Unidade Territorial is highly overall correlated with Pib per capita 2020 and 1 other fieldsHigh correlation
Pib per capita 2020 is highly overall correlated with Cluster and 8 other fieldsHigh correlation
Pib a preços correntes (R$ 1.000) is highly overall correlated with capital (s/n) and 8 other fieldsHigh correlation
Finanças - Investimento per Capita - Invest. Educação - Nota is highly overall correlated with Finanças - Investimento per Capita - Invest. Saúde - Nota and 1 other fieldsHigh correlation
Finanças - Investimento per Capita - Invest. Educação - Meta is highly overall correlated with capital (s/n) and 10 other fieldsHigh correlation
Finanças - Investimento per Capita - Invest. Saúde - Nota is highly overall correlated with Finanças - Investimento per Capita - Invest. Educação - Nota and 1 other fieldsHigh correlation
Finanças - Investimento per Capita - Invest. Saúde - Meta is highly overall correlated with capital (s/n) and 11 other fieldsHigh correlation
Finanças - Investimento per Capita (Indicador) is highly overall correlated with Pib a preços correntes (R$ 1.000) and 3 other fieldsHigh correlation
Finanças - Fiscal - Autonomia - Nota is highly overall correlated with Cluster and 8 other fieldsHigh correlation
Finanças - Fiscal - Gasto com Pessoal - Nota is highly overall correlated with Finanças - Fiscal - Investimentos - Nota and 1 other fieldsHigh correlation
Finanças - Fiscal - Gasto com Pessoal - Meta is highly overall correlated with Cluster and 9 other fieldsHigh correlation
Finanças - Fiscal - Investimentos - Nota is highly overall correlated with Finanças - Fiscal - Gasto com Pessoal - Nota and 2 other fieldsHigh correlation
Finanças - Fiscal - Investimentos - Meta is highly overall correlated with capital (s/n) and 10 other fieldsHigh correlation
Finanças - Fiscal - Liquidez - Nota is highly overall correlated with Finanças - Fiscal - Investimentos - Nota and 1 other fieldsHigh correlation
Finanças - Fiscal (Indicador) is highly overall correlated with Finanças - Fiscal - Autonomia - Nota and 3 other fieldsHigh correlation
capital (s/n) is highly overall correlated with Cluster and 5 other fieldsHigh correlation
Cluster is highly overall correlated with capital (s/n) and 10 other fieldsHigh correlation
Finanças - Fiscal - Autonomia - Meta is highly overall correlated with Cluster and 6 other fieldsHigh correlation
Finanças - Fiscal - Liquidez - Meta is highly overall correlated with Cluster and 6 other fieldsHigh correlation
Finanças - Investimento per Capita - Invest. Educação - Outlier is highly overall correlated with Pib per capita 2020High correlation
nome is uniformly distributedUniform
Nome_UF is uniformly distributedUniform
codigo_municipio has unique valuesUnique
Código IBGE has unique valuesUnique
nome has unique valuesUnique
Nome_UF has unique valuesUnique
Área da Unidade Territorial has unique valuesUnique
Pib per capita 2020 has unique valuesUnique
Pib a preços correntes (R$ 1.000) has unique valuesUnique
Finanças - Investimento per Capita - Invest. Educação - Dado Bruto is an unsupported type, check if it needs cleaning or further analysisUnsupported
Finanças - Investimento per Capita - Invest. Saúde - Dado Bruto is an unsupported type, check if it needs cleaning or further analysisUnsupported
Finanças - Fiscal - Autonomia - Dado Bruto is an unsupported type, check if it needs cleaning or further analysisUnsupported
Finanças - Fiscal - Gasto com Pessoal - Dado Bruto is an unsupported type, check if it needs cleaning or further analysisUnsupported
Finanças - Fiscal - Investimentos - Dado Bruto is an unsupported type, check if it needs cleaning or further analysisUnsupported
Finanças - Fiscal - Liquidez - Dado Bruto is an unsupported type, check if it needs cleaning or further analysisUnsupported
Finanças - Equilíbrio Previdenciário - Indicador Situação Prev. - Dado Bruto is an unsupported type, check if it needs cleaning or further analysisUnsupported
Finanças - Investimento per Capita - Invest. Educação - Nota has 7 (5.0%) zerosZeros
Finanças - Investimento per Capita - Invest. Saúde - Nota has 8 (5.8%) zerosZeros
Finanças - Investimento per Capita (Indicador) has 7 (5.0%) zerosZeros
Finanças - Fiscal - Autonomia - Nota has 84 (60.4%) zerosZeros
Finanças - Fiscal - Gasto com Pessoal - Nota has 23 (16.5%) zerosZeros
Finanças - Fiscal - Investimentos - Nota has 13 (9.4%) zerosZeros
Finanças - Fiscal - Liquidez - Nota has 20 (14.4%) zerosZeros
Finanças - Fiscal (Indicador) has 13 (9.4%) zerosZeros

Reproduction

Analysis started2022-12-08 22:52:45.467904
Analysis finished2022-12-08 22:53:22.441802
Duration36.97 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

ano
Categorical

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2021
139 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters556
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 139
100.0%

Length

2022-12-08T19:53:22.488606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:22.586986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 139
100.0%

Most occurring characters

ValueCountFrequency (%)
2 278
50.0%
0 139
25.0%
1 139
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 556
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 278
50.0%
0 139
25.0%
1 139
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 556
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 278
50.0%
0 139
25.0%
1 139
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 556
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 278
50.0%
0 139
25.0%
1 139
25.0%

codigo_municipio
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct139
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171154.45
Minimum170025
Maximum172210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-12-08T19:53:22.682234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum170025
5-th percentile170109.5
Q1170435
median171240
Q3171825
95-th percentile172097.3
Maximum172210
Range2185
Interquartile range (IQR)1390

Descriptive statistics

Standard deviation687.53881
Coefficient of variation (CV)0.0040170666
Kurtosis-1.4239796
Mean171154.45
Median Absolute Deviation (MAD)630
Skewness-0.12272317
Sum23790468
Variance472709.61
MonotonicityNot monotonic
2022-12-08T19:53:22.814059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
171575 1
 
0.7%
170320 1
 
0.7%
172210 1
 
0.7%
171190 1
 
0.7%
170710 1
 
0.7%
170305 1
 
0.7%
172097 1
 
0.7%
170040 1
 
0.7%
171890 1
 
0.7%
170825 1
 
0.7%
Other values (129) 129
92.8%
ValueCountFrequency (%)
170025 1
0.7%
170030 1
0.7%
170035 1
0.7%
170040 1
0.7%
170070 1
0.7%
170100 1
0.7%
170105 1
0.7%
170110 1
0.7%
170130 1
0.7%
170190 1
0.7%
ValueCountFrequency (%)
172210 1
0.7%
172208 1
0.7%
172130 1
0.7%
172125 1
0.7%
172120 1
0.7%
172110 1
0.7%
172100 1
0.7%
172097 1
0.7%
172093 1
0.7%
172090 1
0.7%

Código IBGE
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct139
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1711548.9
Minimum1700251
Maximum1722107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-12-08T19:53:22.943151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1700251
5-th percentile1701096
Q11704352.5
median1712405
Q31718253.5
95-th percentile1720980.2
Maximum1722107
Range21856
Interquartile range (IQR)13901

Descriptive statistics

Standard deviation6875.6616
Coefficient of variation (CV)0.004017216
Kurtosis-1.4238195
Mean1711548.9
Median Absolute Deviation (MAD)6305
Skewness-0.1227163
Sum2.3790529 × 108
Variance47274722
MonotonicityNot monotonic
2022-12-08T19:53:23.063025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1715754 1
 
0.7%
1703206 1
 
0.7%
1722107 1
 
0.7%
1711902 1
 
0.7%
1707108 1
 
0.7%
1703057 1
 
0.7%
1720978 1
 
0.7%
1700400 1
 
0.7%
1718907 1
 
0.7%
1708254 1
 
0.7%
Other values (129) 129
92.8%
ValueCountFrequency (%)
1700251 1
0.7%
1700301 1
0.7%
1700350 1
0.7%
1700400 1
0.7%
1700707 1
0.7%
1701002 1
0.7%
1701051 1
0.7%
1701101 1
0.7%
1701309 1
0.7%
1701903 1
0.7%
ValueCountFrequency (%)
1722107 1
0.7%
1722081 1
0.7%
1721307 1
0.7%
1721257 1
0.7%
1721208 1
0.7%
1721109 1
0.7%
1721000 1
0.7%
1720978 1
0.7%
1720937 1
0.7%
1720903 1
0.7%

nome
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct139
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
PALMEIROPOLIS
 
1
BERNARDO SAYAO
 
1
XAMBIOA
 
1
LAGOA DA CONFUSAO
 
1
DIVINOPOLIS DO TOCANTINS
 
1
Other values (134)
134 

Length

Max length28
Median length21
Mean length13.906475
Min length4

Characters and Unicode

Total characters1933
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique139 ?
Unique (%)100.0%

Sample

1st rowPALMEIROPOLIS
2nd rowTAGUATINGA
3rd rowDOIS IRMAOS DO TOCANTINS
4th rowAUGUSTINOPOLIS
5th rowBURITI DO TOCANTINS

Common Values

ValueCountFrequency (%)
PALMEIROPOLIS 1
 
0.7%
BERNARDO SAYAO 1
 
0.7%
XAMBIOA 1
 
0.7%
LAGOA DA CONFUSAO 1
 
0.7%
DIVINOPOLIS DO TOCANTINS 1
 
0.7%
BANDEIRANTES DO TOCANTINS 1
 
0.7%
TALISMA 1
 
0.7%
ALMAS 1
 
0.7%
SANTA ROSA DO TOCANTINS 1
 
0.7%
FORTALEZA DO TABOCAO 1
 
0.7%
Other values (129) 129
92.8%

Length

2022-12-08T19:53:23.183892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
do 44
 
15.7%
tocantins 37
 
13.2%
santa 6
 
2.1%
sao 6
 
2.1%
de 4
 
1.4%
da 4
 
1.4%
novo 4
 
1.4%
rio 4
 
1.4%
natividade 3
 
1.1%
araguaia 2
 
0.7%
Other values (153) 166
59.3%

Most occurring characters

ValueCountFrequency (%)
A 312
16.1%
O 215
11.1%
I 183
9.5%
N 171
8.8%
141
 
7.3%
T 135
 
7.0%
S 114
 
5.9%
R 105
 
5.4%
D 99
 
5.1%
C 81
 
4.2%
Other values (18) 377
19.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1791
92.7%
Space Separator 141
 
7.3%
Modifier Symbol 1
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 312
17.4%
O 215
12.0%
I 183
10.2%
N 171
9.5%
T 135
7.5%
S 114
 
6.4%
R 105
 
5.9%
D 99
 
5.5%
C 81
 
4.5%
E 80
 
4.5%
Other values (16) 296
16.5%
Space Separator
ValueCountFrequency (%)
141
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1791
92.7%
Common 142
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 312
17.4%
O 215
12.0%
I 183
10.2%
N 171
9.5%
T 135
7.5%
S 114
 
6.4%
R 105
 
5.9%
D 99
 
5.5%
C 81
 
4.5%
E 80
 
4.5%
Other values (16) 296
16.5%
Common
ValueCountFrequency (%)
141
99.3%
´ 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1932
99.9%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 312
16.1%
O 215
11.1%
I 183
9.5%
N 171
8.9%
141
 
7.3%
T 135
 
7.0%
S 114
 
5.9%
R 105
 
5.4%
D 99
 
5.1%
C 81
 
4.2%
Other values (17) 376
19.5%
None
ValueCountFrequency (%)
´ 1
100.0%

estado (sigla)
Categorical

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
TO
139 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters278
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTO
2nd rowTO
3rd rowTO
4th rowTO
5th rowTO

Common Values

ValueCountFrequency (%)
TO 139
100.0%

Length

2022-12-08T19:53:23.282776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:23.376639image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
to 139
100.0%

Most occurring characters

ValueCountFrequency (%)
T 139
50.0%
O 139
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 278
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 139
50.0%
O 139
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 278
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 139
50.0%
O 139
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 278
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 139
50.0%
O 139
50.0%

Região
Categorical

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Norte
139 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters695
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorte
2nd rowNorte
3rd rowNorte
4th rowNorte
5th rowNorte

Common Values

ValueCountFrequency (%)
Norte 139
100.0%

Length

2022-12-08T19:53:23.448466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:23.539229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
norte 139
100.0%

Most occurring characters

ValueCountFrequency (%)
N 139
20.0%
o 139
20.0%
r 139
20.0%
t 139
20.0%
e 139
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 556
80.0%
Uppercase Letter 139
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 139
25.0%
r 139
25.0%
t 139
25.0%
e 139
25.0%
Uppercase Letter
ValueCountFrequency (%)
N 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 695
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 139
20.0%
o 139
20.0%
r 139
20.0%
t 139
20.0%
e 139
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 695
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 139
20.0%
o 139
20.0%
r 139
20.0%
t 139
20.0%
e 139
20.0%

Nome_UF
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct139
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
PALMEIROPOLIS - TO
 
1
BERNARDO SAYAO - TO
 
1
XAMBIOA - TO
 
1
LAGOA DA CONFUSAO - TO
 
1
DIVINOPOLIS DO TOCANTINS - TO
 
1
Other values (134)
134 

Length

Max length33
Median length26
Mean length18.906475
Min length9

Characters and Unicode

Total characters2628
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique139 ?
Unique (%)100.0%

Sample

1st rowPALMEIROPOLIS - TO
2nd rowTAGUATINGA - TO
3rd rowDOIS IRMAOS DO TOCANTINS - TO
4th rowAUGUSTINOPOLIS - TO
5th rowBURITI DO TOCANTINS - TO

Common Values

ValueCountFrequency (%)
PALMEIROPOLIS - TO 1
 
0.7%
BERNARDO SAYAO - TO 1
 
0.7%
XAMBIOA - TO 1
 
0.7%
LAGOA DA CONFUSAO - TO 1
 
0.7%
DIVINOPOLIS DO TOCANTINS - TO 1
 
0.7%
BANDEIRANTES DO TOCANTINS - TO 1
 
0.7%
TALISMA - TO 1
 
0.7%
ALMAS - TO 1
 
0.7%
SANTA ROSA DO TOCANTINS - TO 1
 
0.7%
FORTALEZA DO TABOCAO - TO 1
 
0.7%
Other values (129) 129
92.8%

Length

2022-12-08T19:53:23.626832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
to 139
24.9%
139
24.9%
do 44
 
7.9%
tocantins 37
 
6.6%
santa 6
 
1.1%
sao 6
 
1.1%
novo 4
 
0.7%
de 4
 
0.7%
rio 4
 
0.7%
da 4
 
0.7%
Other values (155) 171
30.6%

Most occurring characters

ValueCountFrequency (%)
419
15.9%
O 354
13.5%
A 312
11.9%
T 274
10.4%
I 183
 
7.0%
N 171
 
6.5%
- 139
 
5.3%
S 114
 
4.3%
R 105
 
4.0%
D 99
 
3.8%
Other values (19) 458
17.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2069
78.7%
Space Separator 419
 
15.9%
Dash Punctuation 139
 
5.3%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 354
17.1%
A 312
15.1%
T 274
13.2%
I 183
8.8%
N 171
8.3%
S 114
 
5.5%
R 105
 
5.1%
D 99
 
4.8%
C 81
 
3.9%
E 80
 
3.9%
Other values (16) 296
14.3%
Space Separator
ValueCountFrequency (%)
419
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 139
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2069
78.7%
Common 559
 
21.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 354
17.1%
A 312
15.1%
T 274
13.2%
I 183
8.8%
N 171
8.3%
S 114
 
5.5%
R 105
 
5.1%
D 99
 
4.8%
C 81
 
3.9%
E 80
 
3.9%
Other values (16) 296
14.3%
Common
ValueCountFrequency (%)
419
75.0%
- 139
 
24.9%
´ 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2627
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
419
15.9%
O 354
13.5%
A 312
11.9%
T 274
10.4%
I 183
7.0%
N 171
 
6.5%
- 139
 
5.3%
S 114
 
4.3%
R 105
 
4.0%
D 99
 
3.8%
Other values (18) 457
17.4%
None
ValueCountFrequency (%)
´ 1
100.0%

capital (s/n)
Categorical

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
n
138 
s
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters139
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st rown
2nd rown
3rd rown
4th rown
5th rown

Common Values

ValueCountFrequency (%)
n 138
99.3%
s 1
 
0.7%

Length

2022-12-08T19:53:23.726500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:23.834427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
n 138
99.3%
s 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
n 138
99.3%
s 1
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 139
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 138
99.3%
s 1
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 139
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 138
99.3%
s 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 139
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 138
99.3%
s 1
 
0.7%

Cluster
Categorical

Distinct7
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Grupo 1
71 
Grupo 2
58 
Grupo 3
 
3
Grupo 6
 
3
Grupo 4
 
2
Other values (2)
 
2

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters973
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.4%

Sample

1st rowGrupo 1
2nd rowGrupo 1
3rd rowGrupo 1
4th rowGrupo 1
5th rowGrupo 1

Common Values

ValueCountFrequency (%)
Grupo 1 71
51.1%
Grupo 2 58
41.7%
Grupo 3 3
 
2.2%
Grupo 6 3
 
2.2%
Grupo 4 2
 
1.4%
Grupo 7 1
 
0.7%
Grupo 8 1
 
0.7%

Length

2022-12-08T19:53:23.917438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:24.024884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
grupo 139
50.0%
1 71
25.5%
2 58
20.9%
3 3
 
1.1%
6 3
 
1.1%
4 2
 
0.7%
7 1
 
0.4%
8 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
G 139
14.3%
r 139
14.3%
u 139
14.3%
p 139
14.3%
o 139
14.3%
139
14.3%
1 71
7.3%
2 58
6.0%
3 3
 
0.3%
6 3
 
0.3%
Other values (3) 4
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 556
57.1%
Uppercase Letter 139
 
14.3%
Space Separator 139
 
14.3%
Decimal Number 139
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 71
51.1%
2 58
41.7%
3 3
 
2.2%
6 3
 
2.2%
4 2
 
1.4%
7 1
 
0.7%
8 1
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
r 139
25.0%
u 139
25.0%
p 139
25.0%
o 139
25.0%
Uppercase Letter
ValueCountFrequency (%)
G 139
100.0%
Space Separator
ValueCountFrequency (%)
139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 695
71.4%
Common 278
 
28.6%

Most frequent character per script

Common
ValueCountFrequency (%)
139
50.0%
1 71
25.5%
2 58
20.9%
3 3
 
1.1%
6 3
 
1.1%
4 2
 
0.7%
7 1
 
0.4%
8 1
 
0.4%
Latin
ValueCountFrequency (%)
G 139
20.0%
r 139
20.0%
u 139
20.0%
p 139
20.0%
o 139
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 139
14.3%
r 139
14.3%
u 139
14.3%
p 139
14.3%
o 139
14.3%
139
14.3%
1 71
7.3%
2 58
6.0%
3 3
 
0.3%
6 3
 
0.3%
Other values (3) 4
 
0.4%
Distinct138
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11440.633
Minimum1118
Maximum306296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-12-08T19:53:24.144969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1118
5-th percentile2045.4
Q13352
median5130
Q38482.5
95-th percentile27133.6
Maximum306296
Range305178
Interquartile range (IQR)5130.5

Descriptive statistics

Standard deviation31052.838
Coefficient of variation (CV)2.7142587
Kurtosis65.899663
Mean11440.633
Median Absolute Deviation (MAD)2322
Skewness7.6831716
Sum1590248
Variance9.6427877 × 108
MonotonicityNot monotonic
2022-12-08T19:53:24.260600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2729 2
 
1.4%
7676 1
 
0.7%
6979 1
 
0.7%
11520 1
 
0.7%
13676 1
 
0.7%
6943 1
 
0.7%
3592 1
 
0.7%
2812 1
 
0.7%
4448 1
 
0.7%
5519 1
 
0.7%
Other values (128) 128
92.1%
ValueCountFrequency (%)
1118 1
0.7%
1410 1
0.7%
1598 1
0.7%
1735 1
0.7%
1922 1
0.7%
1954 1
0.7%
1986 1
0.7%
2052 1
0.7%
2166 1
0.7%
2171 1
0.7%
ValueCountFrequency (%)
306296 1
0.7%
183381 1
0.7%
87545 1
0.7%
53316 1
0.7%
51891 1
0.7%
36170 1
0.7%
35851 1
0.7%
26165 1
0.7%
22845 1
0.7%
22424 1
0.7%
Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
IBGE/2020
139 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1251
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIBGE/2020
2nd rowIBGE/2020
3rd rowIBGE/2020
4th rowIBGE/2020
5th rowIBGE/2020

Common Values

ValueCountFrequency (%)
IBGE/2020 139
100.0%

Length

2022-12-08T19:53:24.366824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:24.466669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ibge/2020 139
100.0%

Most occurring characters

ValueCountFrequency (%)
2 278
22.2%
0 278
22.2%
I 139
11.1%
B 139
11.1%
G 139
11.1%
E 139
11.1%
/ 139
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 556
44.4%
Uppercase Letter 556
44.4%
Other Punctuation 139
 
11.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 139
25.0%
B 139
25.0%
G 139
25.0%
E 139
25.0%
Decimal Number
ValueCountFrequency (%)
2 278
50.0%
0 278
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 695
55.6%
Latin 556
44.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 139
25.0%
B 139
25.0%
G 139
25.0%
E 139
25.0%
Common
ValueCountFrequency (%)
2 278
40.0%
0 278
40.0%
/ 139
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 278
22.2%
0 278
22.2%
I 139
11.1%
B 139
11.1%
G 139
11.1%
E 139
11.1%
/ 139
11.1%

Área da Unidade Territorial
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct139
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1997.9899
Minimum150.21
Maximum13423.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-12-08T19:53:24.566384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum150.21
5-th percentile250.267
Q1738.985
median1367.61
Q32243.55
95-th percentile6359.793
Maximum13423.39
Range13273.18
Interquartile range (IQR)1504.565

Descriptive statistics

Standard deviation2223.8608
Coefficient of variation (CV)1.1130491
Kurtosis9.1707966
Mean1997.9899
Median Absolute Deviation (MAD)726.14
Skewness2.8030419
Sum277720.6
Variance4945557
MonotonicityNot monotonic
2022-12-08T19:53:24.716958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1703.94 1
 
0.7%
926.89 1
 
0.7%
1186.43 1
 
0.7%
10564.67 1
 
0.7%
2347.43 1
 
0.7%
1541.84 1
 
0.7%
2156.9 1
 
0.7%
4013.24 1
 
0.7%
1796.26 1
 
0.7%
621.56 1
 
0.7%
Other values (129) 129
92.8%
ValueCountFrequency (%)
150.21 1
0.7%
192.94 1
0.7%
200.11 1
0.7%
205.85 1
0.7%
209.57 1
0.7%
222.29 1
0.7%
235.39 1
0.7%
251.92 1
0.7%
269.68 1
0.7%
279.56 1
0.7%
ValueCountFrequency (%)
13423.39 1
0.7%
11260.22 1
0.7%
10564.67 1
0.7%
10013.78 1
0.7%
9681.66 1
0.7%
6491.13 1
0.7%
6408.6 1
0.7%
6354.37 1
0.7%
5786.87 1
0.7%
5723.23 1
0.7%
Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
IBGE/2010
139 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1251
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIBGE/2010
2nd rowIBGE/2010
3rd rowIBGE/2010
4th rowIBGE/2010
5th rowIBGE/2010

Common Values

ValueCountFrequency (%)
IBGE/2010 139
100.0%

Length

2022-12-08T19:53:24.836799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:24.939945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ibge/2010 139
100.0%

Most occurring characters

ValueCountFrequency (%)
0 278
22.2%
I 139
11.1%
B 139
11.1%
G 139
11.1%
E 139
11.1%
/ 139
11.1%
2 139
11.1%
1 139
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 556
44.4%
Uppercase Letter 556
44.4%
Other Punctuation 139
 
11.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 139
25.0%
B 139
25.0%
G 139
25.0%
E 139
25.0%
Decimal Number
ValueCountFrequency (%)
0 278
50.0%
2 139
25.0%
1 139
25.0%
Other Punctuation
ValueCountFrequency (%)
/ 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 695
55.6%
Latin 556
44.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 278
40.0%
/ 139
20.0%
2 139
20.0%
1 139
20.0%
Latin
ValueCountFrequency (%)
I 139
25.0%
B 139
25.0%
G 139
25.0%
E 139
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 278
22.2%
I 139
11.1%
B 139
11.1%
G 139
11.1%
E 139
11.1%
/ 139
11.1%
2 139
11.1%
1 139
11.1%

Pib per capita 2020
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct139
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18970.471
Minimum8211.68
Maximum80738.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-12-08T19:53:25.039733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum8211.68
5-th percentile9724.808
Q111695.16
median15640.65
Q322138
95-th percentile37286.594
Maximum80738.87
Range72527.19
Interquartile range (IQR)10442.84

Descriptive statistics

Standard deviation11011.299
Coefficient of variation (CV)0.58044414
Kurtosis10.303759
Mean18970.471
Median Absolute Deviation (MAD)4632.28
Skewness2.6581216
Sum2636895.5
Variance1.212487 × 108
MonotonicityNot monotonic
2022-12-08T19:53:25.158008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15543.8 1
 
0.7%
16725.68 1
 
0.7%
25166.89 1
 
0.7%
37107.01 1
 
0.7%
17949.32 1
 
0.7%
34257.69 1
 
0.7%
29682.14 1
 
0.7%
21314.69 1
 
0.7%
31072.56 1
 
0.7%
72737.58 1
 
0.7%
Other values (129) 129
92.8%
ValueCountFrequency (%)
8211.68 1
0.7%
8501.44 1
0.7%
8623.29 1
0.7%
8731.58 1
0.7%
8836.5 1
0.7%
9386.15 1
0.7%
9623.27 1
0.7%
9736.09 1
0.7%
9813.95 1
0.7%
9887 1
0.7%
ValueCountFrequency (%)
80738.87 1
0.7%
72737.58 1
0.7%
51358.01 1
0.7%
45449.34 1
0.7%
43272.03 1
0.7%
39844.35 1
0.7%
38902.85 1
0.7%
37107.01 1
0.7%
35362.17 1
0.7%
34257.69 1
0.7%
Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
IBGE/2020
139 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1251
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIBGE/2020
2nd rowIBGE/2020
3rd rowIBGE/2020
4th rowIBGE/2020
5th rowIBGE/2020

Common Values

ValueCountFrequency (%)
IBGE/2020 139
100.0%

Length

2022-12-08T19:53:25.269581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:25.364804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ibge/2020 139
100.0%

Most occurring characters

ValueCountFrequency (%)
2 278
22.2%
0 278
22.2%
I 139
11.1%
B 139
11.1%
G 139
11.1%
E 139
11.1%
/ 139
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 556
44.4%
Uppercase Letter 556
44.4%
Other Punctuation 139
 
11.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 139
25.0%
B 139
25.0%
G 139
25.0%
E 139
25.0%
Decimal Number
ValueCountFrequency (%)
2 278
50.0%
0 278
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 695
55.6%
Latin 556
44.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 139
25.0%
B 139
25.0%
G 139
25.0%
E 139
25.0%
Common
ValueCountFrequency (%)
2 278
40.0%
0 278
40.0%
/ 139
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 278
22.2%
0 278
22.2%
I 139
11.1%
B 139
11.1%
G 139
11.1%
E 139
11.1%
/ 139
11.1%

Pib a preços correntes (R$ 1.000)
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct139
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean263228.02
Minimum20542.635
Maximum9891489.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-12-08T19:53:25.466830image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum20542.635
5-th percentile27422.645
Q147547.968
median83466.178
Q3162088.35
95-th percentile560283.95
Maximum9891489.3
Range9870946.7
Interquartile range (IQR)114540.38

Descriptive statistics

Standard deviation942919.99
Coefficient of variation (CV)3.5821414
Kurtosis82.596024
Mean263228.02
Median Absolute Deviation (MAD)42710.274
Skewness8.6084381
Sum36588695
Variance8.8909811 × 1011
MonotonicityNot monotonic
2022-12-08T19:53:25.603457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119314.2088 1
 
0.7%
74395.82464 1
 
0.7%
289922.5728 1
 
0.7%
507475.4688 1
 
0.7%
124622.1288 1
 
0.7%
123053.6225 1
 
0.7%
83466.17768 1
 
0.7%
148755.2215 1
 
0.7%
150577.6258 1
 
0.7%
189263.1832 1
 
0.7%
Other values (129) 129
92.8%
ValueCountFrequency (%)
20542.6351 1
0.7%
21852.61804 1
0.7%
21941.29834 1
0.7%
23563.89234 1
0.7%
24068.4885 1
0.7%
24501.28416 1
0.7%
27361.42596 1
0.7%
27429.44676 1
0.7%
28813.7399 1
0.7%
29718.9072 1
0.7%
ValueCountFrequency (%)
9891489.331 1
0.7%
4532437.461 1
0.7%
2249067.819 1
0.7%
1697072.805 1
0.7%
1261793.491 1
0.7%
744012.5764 1
0.7%
663853.4086 1
0.7%
548776.2325 1
0.7%
539620.0138 1
0.7%
508440.76 1
0.7%
Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
IBGE/2020
139 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1251
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIBGE/2020
2nd rowIBGE/2020
3rd rowIBGE/2020
4th rowIBGE/2020
5th rowIBGE/2020

Common Values

ValueCountFrequency (%)
IBGE/2020 139
100.0%

Length

2022-12-08T19:53:25.718821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:25.813809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ibge/2020 139
100.0%

Most occurring characters

ValueCountFrequency (%)
2 278
22.2%
0 278
22.2%
I 139
11.1%
B 139
11.1%
G 139
11.1%
E 139
11.1%
/ 139
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 556
44.4%
Uppercase Letter 556
44.4%
Other Punctuation 139
 
11.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 139
25.0%
B 139
25.0%
G 139
25.0%
E 139
25.0%
Decimal Number
ValueCountFrequency (%)
2 278
50.0%
0 278
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 695
55.6%
Latin 556
44.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 139
25.0%
B 139
25.0%
G 139
25.0%
E 139
25.0%
Common
ValueCountFrequency (%)
2 278
40.0%
0 278
40.0%
/ 139
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 278
22.2%
0 278
22.2%
I 139
11.1%
B 139
11.1%
G 139
11.1%
E 139
11.1%
/ 139
11.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Distinct111
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.974964
Minimum0
Maximum10
Zeros7
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-12-08T19:53:25.918114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.458
Q14.1
median5.84
Q38.26
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)4.16

Descriptive statistics

Standard deviation2.6966351
Coefficient of variation (CV)0.45132241
Kurtosis-0.55258597
Mean5.974964
Median Absolute Deviation (MAD)1.99
Skewness-0.21149776
Sum830.52
Variance7.2718411
MonotonicityNot monotonic
2022-12-08T19:53:26.061590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 16
 
11.5%
0 7
 
5.0%
3.43 2
 
1.4%
2.65 2
 
1.4%
5.73 2
 
1.4%
5.82 2
 
1.4%
3.54 2
 
1.4%
8.39 2
 
1.4%
3.67 2
 
1.4%
2.62 1
 
0.7%
Other values (101) 101
72.7%
ValueCountFrequency (%)
0 7
5.0%
1.62 1
 
0.7%
1.99 1
 
0.7%
2.33 1
 
0.7%
2.42 1
 
0.7%
2.62 1
 
0.7%
2.65 2
 
1.4%
2.78 1
 
0.7%
2.93 1
 
0.7%
3 1
 
0.7%
ValueCountFrequency (%)
10 16
11.5%
9.86 1
 
0.7%
9.78 1
 
0.7%
9.73 1
 
0.7%
9.59 1
 
0.7%
9.56 1
 
0.7%
9.47 1
 
0.7%
9.43 1
 
0.7%
9.23 1
 
0.7%
9.16 1
 
0.7%
Distinct7
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1415.6267
Minimum881.88868
Maximum1530.445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-12-08T19:53:26.163546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum881.88868
5-th percentile1248.5487
Q11354.9595
median1354.9595
Q31530.445
95-th percentile1530.445
Maximum1530.445
Range648.55636
Interquartile range (IQR)175.48558

Descriptive statistics

Standard deviation111.09372
Coefficient of variation (CV)0.078476707
Kurtosis2.6395391
Mean1415.6267
Median Absolute Deviation (MAD)0
Skewness-0.91741945
Sum196772.11
Variance12341.815
MonotonicityNot monotonic
2022-12-08T19:53:26.252891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1354.959465 71
51.1%
1530.445047 58
41.7%
1248.735078 3
 
2.2%
1246.871303 3
 
2.2%
1170.632635 2
 
1.4%
881.8886823 1
 
0.7%
1094.205857 1
 
0.7%
ValueCountFrequency (%)
881.8886823 1
 
0.7%
1094.205857 1
 
0.7%
1170.632635 2
 
1.4%
1246.871303 3
 
2.2%
1248.735078 3
 
2.2%
1354.959465 71
51.1%
1530.445047 58
41.7%
ValueCountFrequency (%)
1530.445047 58
41.7%
1354.959465 71
51.1%
1248.735078 3
 
2.2%
1246.871303 3
 
2.2%
1170.632635 2
 
1.4%
1094.205857 1
 
0.7%
881.8886823 1
 
0.7%
Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Não
134 
Sim
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters417
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNão
2nd rowNão
3rd rowNão
4th rowNão
5th rowNão

Common Values

ValueCountFrequency (%)
Não 134
96.4%
Sim 5
 
3.6%

Length

2022-12-08T19:53:26.350060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:26.443809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
não 134
96.4%
sim 5
 
3.6%

Most occurring characters

ValueCountFrequency (%)
N 134
32.1%
ã 134
32.1%
o 134
32.1%
S 5
 
1.2%
i 5
 
1.2%
m 5
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 278
66.7%
Uppercase Letter 139
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
ã 134
48.2%
o 134
48.2%
i 5
 
1.8%
m 5
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
N 134
96.4%
S 5
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 417
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 134
32.1%
ã 134
32.1%
o 134
32.1%
S 5
 
1.2%
i 5
 
1.2%
m 5
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 283
67.9%
None 134
32.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 134
47.3%
o 134
47.3%
S 5
 
1.8%
i 5
 
1.8%
m 5
 
1.8%
None
ValueCountFrequency (%)
ã 134
100.0%
Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
STN/2019
139 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1112
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTN/2019
2nd rowSTN/2019
3rd rowSTN/2019
4th rowSTN/2019
5th rowSTN/2019

Common Values

ValueCountFrequency (%)
STN/2019 139
100.0%

Length

2022-12-08T19:53:26.522223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:26.623956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
stn/2019 139
100.0%

Most occurring characters

ValueCountFrequency (%)
S 139
12.5%
T 139
12.5%
N 139
12.5%
/ 139
12.5%
2 139
12.5%
0 139
12.5%
1 139
12.5%
9 139
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 556
50.0%
Uppercase Letter 417
37.5%
Other Punctuation 139
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 139
25.0%
0 139
25.0%
1 139
25.0%
9 139
25.0%
Uppercase Letter
ValueCountFrequency (%)
S 139
33.3%
T 139
33.3%
N 139
33.3%
Other Punctuation
ValueCountFrequency (%)
/ 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 695
62.5%
Latin 417
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 139
20.0%
2 139
20.0%
0 139
20.0%
1 139
20.0%
9 139
20.0%
Latin
ValueCountFrequency (%)
S 139
33.3%
T 139
33.3%
N 139
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 139
12.5%
T 139
12.5%
N 139
12.5%
/ 139
12.5%
2 139
12.5%
0 139
12.5%
1 139
12.5%
9 139
12.5%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Distinct109
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1530216
Minimum0
Maximum10
Zeros8
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-12-08T19:53:26.716331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.01
median4.4
Q37.49
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)4.48

Descriptive statistics

Standard deviation2.9789624
Coefficient of variation (CV)0.57810012
Kurtosis-1.0126168
Mean5.1530216
Median Absolute Deviation (MAD)2.04
Skewness0.23742321
Sum716.27
Variance8.8742169
MonotonicityNot monotonic
2022-12-08T19:53:26.858927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 14
 
10.1%
0 8
 
5.8%
3.42 2
 
1.4%
6.52 2
 
1.4%
4.32 2
 
1.4%
8.64 2
 
1.4%
3.66 2
 
1.4%
6.43 2
 
1.4%
3.5 2
 
1.4%
4 2
 
1.4%
Other values (99) 101
72.7%
ValueCountFrequency (%)
0 8
5.8%
1.17 1
 
0.7%
1.31 1
 
0.7%
1.36 1
 
0.7%
1.44 1
 
0.7%
1.47 1
 
0.7%
1.53 1
 
0.7%
1.61 1
 
0.7%
1.75 1
 
0.7%
1.84 1
 
0.7%
ValueCountFrequency (%)
10 14
10.1%
9.9 1
 
0.7%
9.83 1
 
0.7%
9.63 1
 
0.7%
9.62 1
 
0.7%
9.57 1
 
0.7%
9.52 1
 
0.7%
9.45 1
 
0.7%
9.4 1
 
0.7%
9.24 1
 
0.7%
Distinct7
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1193.7645
Minimum673.54644
Maximum1499.3691
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-12-08T19:53:26.959256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum673.54644
5-th percentile980.53246
Q1980.53246
median980.53246
Q31499.3691
95-th percentile1499.3691
Maximum1499.3691
Range825.82261
Interquartile range (IQR)518.8366

Descriptive statistics

Standard deviation265.09849
Coefficient of variation (CV)0.22206933
Kurtosis-1.704197
Mean1193.7645
Median Absolute Deviation (MAD)0
Skewness0.19179886
Sum165933.27
Variance70277.211
MonotonicityNot monotonic
2022-12-08T19:53:27.031754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
980.5324596 71
51.1%
1499.369056 58
41.7%
673.5464413 3
 
2.2%
1075.024453 3
 
2.2%
991.0259585 2
 
1.4%
859.4854444 1
 
0.7%
1264.808039 1
 
0.7%
ValueCountFrequency (%)
673.5464413 3
 
2.2%
859.4854444 1
 
0.7%
980.5324596 71
51.1%
991.0259585 2
 
1.4%
1075.024453 3
 
2.2%
1264.808039 1
 
0.7%
1499.369056 58
41.7%
ValueCountFrequency (%)
1499.369056 58
41.7%
1264.808039 1
 
0.7%
1075.024453 3
 
2.2%
991.0259585 2
 
1.4%
980.5324596 71
51.1%
859.4854444 1
 
0.7%
673.5464413 3
 
2.2%
Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Não
134 
Sim
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters417
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNão
2nd rowNão
3rd rowNão
4th rowNão
5th rowNão

Common Values

ValueCountFrequency (%)
Não 134
96.4%
Sim 5
 
3.6%

Length

2022-12-08T19:53:27.118819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:27.214468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
não 134
96.4%
sim 5
 
3.6%

Most occurring characters

ValueCountFrequency (%)
N 134
32.1%
ã 134
32.1%
o 134
32.1%
S 5
 
1.2%
i 5
 
1.2%
m 5
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 278
66.7%
Uppercase Letter 139
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
ã 134
48.2%
o 134
48.2%
i 5
 
1.8%
m 5
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
N 134
96.4%
S 5
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 417
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 134
32.1%
ã 134
32.1%
o 134
32.1%
S 5
 
1.2%
i 5
 
1.2%
m 5
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 283
67.9%
None 134
32.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 134
47.3%
o 134
47.3%
S 5
 
1.8%
i 5
 
1.8%
m 5
 
1.8%
None
ValueCountFrequency (%)
ã 134
100.0%
Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
STN/2019
139 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1112
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTN/2019
2nd rowSTN/2019
3rd rowSTN/2019
4th rowSTN/2019
5th rowSTN/2019

Common Values

ValueCountFrequency (%)
STN/2019 139
100.0%

Length

2022-12-08T19:53:27.291946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:27.382648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
stn/2019 139
100.0%

Most occurring characters

ValueCountFrequency (%)
S 139
12.5%
T 139
12.5%
N 139
12.5%
/ 139
12.5%
2 139
12.5%
0 139
12.5%
1 139
12.5%
9 139
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 556
50.0%
Uppercase Letter 417
37.5%
Other Punctuation 139
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 139
25.0%
0 139
25.0%
1 139
25.0%
9 139
25.0%
Uppercase Letter
ValueCountFrequency (%)
S 139
33.3%
T 139
33.3%
N 139
33.3%
Other Punctuation
ValueCountFrequency (%)
/ 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 695
62.5%
Latin 417
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 139
20.0%
2 139
20.0%
0 139
20.0%
1 139
20.0%
9 139
20.0%
Latin
ValueCountFrequency (%)
S 139
33.3%
T 139
33.3%
N 139
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 139
12.5%
T 139
12.5%
N 139
12.5%
/ 139
12.5%
2 139
12.5%
0 139
12.5%
1 139
12.5%
9 139
12.5%

Finanças - Investimento per Capita (Indicador)
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct127
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5639928
Minimum0
Maximum10
Zeros7
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-12-08T19:53:27.764157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.701
Q13.925
median5.39
Q37.34
95-th percentile9.818
Maximum10
Range10
Interquartile range (IQR)3.415

Descriptive statistics

Standard deviation2.5122092
Coefficient of variation (CV)0.45151194
Kurtosis-0.3641579
Mean5.5639928
Median Absolute Deviation (MAD)1.61
Skewness-0.079441634
Sum773.395
Variance6.311195
MonotonicityNot monotonic
2022-12-08T19:53:27.899221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
5.0%
10 5
 
3.6%
4.265 2
 
1.4%
5.23 2
 
1.4%
6.05 1
 
0.7%
9.81 1
 
0.7%
3.355 1
 
0.7%
2.775 1
 
0.7%
3.85 1
 
0.7%
8.215 1
 
0.7%
Other values (117) 117
84.2%
ValueCountFrequency (%)
0 7
5.0%
1.89 1
 
0.7%
2.18 1
 
0.7%
2.25 1
 
0.7%
2.26 1
 
0.7%
2.62 1
 
0.7%
2.67 1
 
0.7%
2.695 1
 
0.7%
2.705 1
 
0.7%
2.775 1
 
0.7%
ValueCountFrequency (%)
10 5
3.6%
9.915 1
 
0.7%
9.89 1
 
0.7%
9.81 1
 
0.7%
9.78 1
 
0.7%
9.7 1
 
0.7%
9.62 1
 
0.7%
9.61 1
 
0.7%
9.555 1
 
0.7%
9.4 1
 
0.7%

Finanças - Fiscal - Autonomia - Dado Bruto
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size2.2 KiB

Finanças - Fiscal - Autonomia - Nota
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct46
Distinct (%)33.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0605755
Minimum0
Maximum10
Zeros84
Zeros (%)60.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-12-08T19:53:28.028042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.17
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)3.17

Descriptive statistics

Standard deviation3.3262282
Coefficient of variation (CV)1.6142229
Kurtosis0.65477158
Mean2.0605755
Median Absolute Deviation (MAD)0
Skewness1.4516812
Sum286.42
Variance11.063794
MonotonicityNot monotonic
2022-12-08T19:53:28.148931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 84
60.4%
10 11
 
7.9%
2.72 1
 
0.7%
5.71 1
 
0.7%
4.39 1
 
0.7%
2.2 1
 
0.7%
2.1 1
 
0.7%
0.37 1
 
0.7%
3.7 1
 
0.7%
0.88 1
 
0.7%
Other values (36) 36
25.9%
ValueCountFrequency (%)
0 84
60.4%
0.19 1
 
0.7%
0.29 1
 
0.7%
0.37 1
 
0.7%
0.8 1
 
0.7%
0.82 1
 
0.7%
0.87 1
 
0.7%
0.88 1
 
0.7%
1.13 1
 
0.7%
1.55 1
 
0.7%
ValueCountFrequency (%)
10 11
7.9%
9.14 1
 
0.7%
8.89 1
 
0.7%
8.86 1
 
0.7%
8.38 1
 
0.7%
8.27 1
 
0.7%
8.23 1
 
0.7%
7.69 1
 
0.7%
7.41 1
 
0.7%
6.52 1
 
0.7%
Distinct3
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
0.31610000000000016
71 
1.0
65 
0.338
 
3

Length

Max length19
Median length19
Mean length11.215827
Min length3

Characters and Unicode

Total characters1559
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.31610000000000016
2nd row0.31610000000000016
3rd row0.31610000000000016
4th row0.31610000000000016
5th row0.31610000000000016

Common Values

ValueCountFrequency (%)
0.31610000000000016 71
51.1%
1.0 65
46.8%
0.338 3
 
2.2%

Length

2022-12-08T19:53:28.268176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:28.372222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.31610000000000016 71
51.1%
1.0 65
46.8%
0.338 3
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 920
59.0%
1 278
 
17.8%
6 142
 
9.1%
. 139
 
8.9%
3 77
 
4.9%
8 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1420
91.1%
Other Punctuation 139
 
8.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 920
64.8%
1 278
 
19.6%
6 142
 
10.0%
3 77
 
5.4%
8 3
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1559
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 920
59.0%
1 278
 
17.8%
6 142
 
9.1%
. 139
 
8.9%
3 77
 
4.9%
8 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1559
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 920
59.0%
1 278
 
17.8%
6 142
 
9.1%
. 139
 
8.9%
3 77
 
4.9%
8 3
 
0.2%
Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Não
139 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNão
2nd rowNão
3rd rowNão
4th rowNão
5th rowNão

Common Values

ValueCountFrequency (%)
Não 139
100.0%

Length

2022-12-08T19:53:28.457087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:28.546780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
não 139
100.0%

Most occurring characters

ValueCountFrequency (%)
N 139
33.3%
ã 139
33.3%
o 139
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 278
66.7%
Uppercase Letter 139
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
ã 139
50.0%
o 139
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 417
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 139
33.3%
ã 139
33.3%
o 139
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 278
66.7%
None 139
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 139
50.0%
o 139
50.0%
None
ValueCountFrequency (%)
ã 139
100.0%
Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Firjan/2019
139 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1529
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirjan/2019
2nd rowFirjan/2019
3rd rowFirjan/2019
4th rowFirjan/2019
5th rowFirjan/2019

Common Values

ValueCountFrequency (%)
Firjan/2019 139
100.0%

Length

2022-12-08T19:53:28.626129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:28.723886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
firjan/2019 139
100.0%

Most occurring characters

ValueCountFrequency (%)
F 139
9.1%
i 139
9.1%
r 139
9.1%
j 139
9.1%
a 139
9.1%
n 139
9.1%
/ 139
9.1%
2 139
9.1%
0 139
9.1%
1 139
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 695
45.5%
Decimal Number 556
36.4%
Uppercase Letter 139
 
9.1%
Other Punctuation 139
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 139
20.0%
r 139
20.0%
j 139
20.0%
a 139
20.0%
n 139
20.0%
Decimal Number
ValueCountFrequency (%)
2 139
25.0%
0 139
25.0%
1 139
25.0%
9 139
25.0%
Uppercase Letter
ValueCountFrequency (%)
F 139
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 834
54.5%
Common 695
45.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 139
16.7%
i 139
16.7%
r 139
16.7%
j 139
16.7%
a 139
16.7%
n 139
16.7%
Common
ValueCountFrequency (%)
/ 139
20.0%
2 139
20.0%
0 139
20.0%
1 139
20.0%
9 139
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1529
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 139
9.1%
i 139
9.1%
r 139
9.1%
j 139
9.1%
a 139
9.1%
n 139
9.1%
/ 139
9.1%
2 139
9.1%
0 139
9.1%
1 139
9.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB

Finanças - Fiscal - Gasto com Pessoal - Nota
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct95
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9452518
Minimum0
Maximum10
Zeros23
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-12-08T19:53:28.818369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.37
median4.93
Q37.54
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5.17

Descriptive statistics

Standard deviation3.3898464
Coefficient of variation (CV)0.68547498
Kurtosis-1.1858903
Mean4.9452518
Median Absolute Deviation (MAD)2.57
Skewness-0.009336835
Sum687.39
Variance11.491058
MonotonicityNot monotonic
2022-12-08T19:53:28.938500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
 
16.5%
10 18
 
12.9%
4.32 2
 
1.4%
6.57 2
 
1.4%
3.37 2
 
1.4%
5.78 2
 
1.4%
2.79 2
 
1.4%
6.6 1
 
0.7%
5.33 1
 
0.7%
1.99 1
 
0.7%
Other values (85) 85
61.2%
ValueCountFrequency (%)
0 23
16.5%
0.22 1
 
0.7%
0.26 1
 
0.7%
0.71 1
 
0.7%
0.92 1
 
0.7%
1.27 1
 
0.7%
1.28 1
 
0.7%
1.56 1
 
0.7%
1.8 1
 
0.7%
1.99 1
 
0.7%
ValueCountFrequency (%)
10 18
12.9%
9.98 1
 
0.7%
9.84 1
 
0.7%
9.75 1
 
0.7%
9.73 1
 
0.7%
9.49 1
 
0.7%
9.47 1
 
0.7%
8.59 1
 
0.7%
8.55 1
 
0.7%
8.49 1
 
0.7%
Distinct6
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86973165
Minimum0.7195
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-12-08T19:53:29.036120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.7195
5-th percentile0.7822
Q10.7822
median0.7822
Q30.972
95-th percentile0.972
Maximum1
Range0.2805
Interquartile range (IQR)0.1898

Descriptive statistics

Standard deviation0.097109715
Coefficient of variation (CV)0.1116548
Kurtosis-1.9368724
Mean0.86973165
Median Absolute Deviation (MAD)0
Skewness0.11387345
Sum120.8927
Variance0.0094302967
MonotonicityNot monotonic
2022-12-08T19:53:29.109818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.7822 71
51.1%
0.972 58
41.7%
1 4
 
2.9%
0.7195 3
 
2.2%
0.9366 2
 
1.4%
0.9488 1
 
0.7%
ValueCountFrequency (%)
0.7195 3
 
2.2%
0.7822 71
51.1%
0.9366 2
 
1.4%
0.9488 1
 
0.7%
0.972 58
41.7%
1 4
 
2.9%
ValueCountFrequency (%)
1 4
 
2.9%
0.972 58
41.7%
0.9488 1
 
0.7%
0.9366 2
 
1.4%
0.7822 71
51.1%
0.7195 3
 
2.2%
Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Não
139 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNão
2nd rowNão
3rd rowNão
4th rowNão
5th rowNão

Common Values

ValueCountFrequency (%)
Não 139
100.0%

Length

2022-12-08T19:53:29.201572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:29.293327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
não 139
100.0%

Most occurring characters

ValueCountFrequency (%)
N 139
33.3%
ã 139
33.3%
o 139
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 278
66.7%
Uppercase Letter 139
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
ã 139
50.0%
o 139
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 417
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 139
33.3%
ã 139
33.3%
o 139
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 278
66.7%
None 139
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 139
50.0%
o 139
50.0%
None
ValueCountFrequency (%)
ã 139
100.0%
Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Firjan/2019
139 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1529
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirjan/2019
2nd rowFirjan/2019
3rd rowFirjan/2019
4th rowFirjan/2019
5th rowFirjan/2019

Common Values

ValueCountFrequency (%)
Firjan/2019 139
100.0%

Length

2022-12-08T19:53:29.374113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:29.466327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
firjan/2019 139
100.0%

Most occurring characters

ValueCountFrequency (%)
F 139
9.1%
i 139
9.1%
r 139
9.1%
j 139
9.1%
a 139
9.1%
n 139
9.1%
/ 139
9.1%
2 139
9.1%
0 139
9.1%
1 139
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 695
45.5%
Decimal Number 556
36.4%
Uppercase Letter 139
 
9.1%
Other Punctuation 139
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 139
20.0%
r 139
20.0%
j 139
20.0%
a 139
20.0%
n 139
20.0%
Decimal Number
ValueCountFrequency (%)
2 139
25.0%
0 139
25.0%
1 139
25.0%
9 139
25.0%
Uppercase Letter
ValueCountFrequency (%)
F 139
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 834
54.5%
Common 695
45.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 139
16.7%
i 139
16.7%
r 139
16.7%
j 139
16.7%
a 139
16.7%
n 139
16.7%
Common
ValueCountFrequency (%)
/ 139
20.0%
2 139
20.0%
0 139
20.0%
1 139
20.0%
9 139
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1529
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 139
9.1%
i 139
9.1%
r 139
9.1%
j 139
9.1%
a 139
9.1%
n 139
9.1%
/ 139
9.1%
2 139
9.1%
0 139
9.1%
1 139
9.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB

Finanças - Fiscal - Investimentos - Nota
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct102
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4174101
Minimum0
Maximum10
Zeros13
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-12-08T19:53:29.559026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.105
median5.4
Q37.875
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)4.77

Descriptive statistics

Standard deviation3.0874295
Coefficient of variation (CV)0.56990876
Kurtosis-0.98419232
Mean5.4174101
Median Absolute Deviation (MAD)2.37
Skewness-0.093941547
Sum753.02
Variance9.5322208
MonotonicityNot monotonic
2022-12-08T19:53:29.681910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 18
 
12.9%
0 13
 
9.4%
5.8 2
 
1.4%
7.43 2
 
1.4%
4.5 2
 
1.4%
6.02 2
 
1.4%
4.76 2
 
1.4%
8.13 2
 
1.4%
4.16 2
 
1.4%
2.72 2
 
1.4%
Other values (92) 92
66.2%
ValueCountFrequency (%)
0 13
9.4%
0.84 1
 
0.7%
1.18 1
 
0.7%
1.24 1
 
0.7%
1.87 1
 
0.7%
1.95 1
 
0.7%
1.96 1
 
0.7%
2.02 1
 
0.7%
2.03 1
 
0.7%
2.08 1
 
0.7%
ValueCountFrequency (%)
10 18
12.9%
9.62 1
 
0.7%
9.31 1
 
0.7%
9.28 1
 
0.7%
9.21 1
 
0.7%
9.16 1
 
0.7%
9.14 1
 
0.7%
9.06 1
 
0.7%
9.04 1
 
0.7%
8.99 1
 
0.7%
Distinct7
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.93559712
Minimum0.632
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-12-08T19:53:29.780990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.632
5-th percentile0.78666
Q10.914
median0.914
Q31
95-th percentile1
Maximum1
Range0.368
Interquartile range (IQR)0.086

Descriptive statistics

Standard deviation0.075765963
Coefficient of variation (CV)0.080981398
Kurtosis4.7663661
Mean0.93559712
Median Absolute Deviation (MAD)0
Skewness-1.9232503
Sum130.048
Variance0.0057404812
MonotonicityNot monotonic
2022-12-08T19:53:29.861279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.914 71
51.1%
1 58
41.7%
0.793 3
 
2.2%
0.676 3
 
2.2%
0.7296 2
 
1.4%
0.6558 1
 
0.7%
0.632 1
 
0.7%
ValueCountFrequency (%)
0.632 1
 
0.7%
0.6558 1
 
0.7%
0.676 3
 
2.2%
0.7296 2
 
1.4%
0.793 3
 
2.2%
0.914 71
51.1%
1 58
41.7%
ValueCountFrequency (%)
1 58
41.7%
0.914 71
51.1%
0.793 3
 
2.2%
0.7296 2
 
1.4%
0.676 3
 
2.2%
0.6558 1
 
0.7%
0.632 1
 
0.7%
Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Não
139 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNão
2nd rowNão
3rd rowNão
4th rowNão
5th rowNão

Common Values

ValueCountFrequency (%)
Não 139
100.0%

Length

2022-12-08T19:53:29.962044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:30.051914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
não 139
100.0%

Most occurring characters

ValueCountFrequency (%)
N 139
33.3%
ã 139
33.3%
o 139
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 278
66.7%
Uppercase Letter 139
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
ã 139
50.0%
o 139
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 417
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 139
33.3%
ã 139
33.3%
o 139
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 278
66.7%
None 139
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 139
50.0%
o 139
50.0%
None
ValueCountFrequency (%)
ã 139
100.0%
Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Firjan/2019
139 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1529
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirjan/2019
2nd rowFirjan/2019
3rd rowFirjan/2019
4th rowFirjan/2019
5th rowFirjan/2019

Common Values

ValueCountFrequency (%)
Firjan/2019 139
100.0%

Length

2022-12-08T19:53:30.126102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:30.218055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
firjan/2019 139
100.0%

Most occurring characters

ValueCountFrequency (%)
F 139
9.1%
i 139
9.1%
r 139
9.1%
j 139
9.1%
a 139
9.1%
n 139
9.1%
/ 139
9.1%
2 139
9.1%
0 139
9.1%
1 139
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 695
45.5%
Decimal Number 556
36.4%
Uppercase Letter 139
 
9.1%
Other Punctuation 139
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 139
20.0%
r 139
20.0%
j 139
20.0%
a 139
20.0%
n 139
20.0%
Decimal Number
ValueCountFrequency (%)
2 139
25.0%
0 139
25.0%
1 139
25.0%
9 139
25.0%
Uppercase Letter
ValueCountFrequency (%)
F 139
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 834
54.5%
Common 695
45.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 139
16.7%
i 139
16.7%
r 139
16.7%
j 139
16.7%
a 139
16.7%
n 139
16.7%
Common
ValueCountFrequency (%)
/ 139
20.0%
2 139
20.0%
0 139
20.0%
1 139
20.0%
9 139
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1529
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 139
9.1%
i 139
9.1%
r 139
9.1%
j 139
9.1%
a 139
9.1%
n 139
9.1%
/ 139
9.1%
2 139
9.1%
0 139
9.1%
1 139
9.1%

Finanças - Fiscal - Liquidez - Dado Bruto
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size2.2 KiB

Finanças - Fiscal - Liquidez - Nota
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct95
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4888489
Minimum0
Maximum10
Zeros20
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-12-08T19:53:30.329217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.765
median5.6
Q36.85
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)2.085

Descriptive statistics

Standard deviation2.7947295
Coefficient of variation (CV)0.50916496
Kurtosis0.11607467
Mean5.4888489
Median Absolute Deviation (MAD)1.06
Skewness-0.52414211
Sum762.95
Variance7.8105132
MonotonicityNot monotonic
2022-12-08T19:53:30.455256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
 
14.4%
10 14
 
10.1%
4.85 3
 
2.2%
7.56 2
 
1.4%
5.17 2
 
1.4%
5.02 2
 
1.4%
5.6 2
 
1.4%
4.23 2
 
1.4%
5.28 2
 
1.4%
6.48 2
 
1.4%
Other values (85) 88
63.3%
ValueCountFrequency (%)
0 20
14.4%
4.05 1
 
0.7%
4.18 1
 
0.7%
4.23 2
 
1.4%
4.24 1
 
0.7%
4.32 1
 
0.7%
4.44 1
 
0.7%
4.46 1
 
0.7%
4.51 1
 
0.7%
4.52 1
 
0.7%
ValueCountFrequency (%)
10 14
10.1%
9.92 1
 
0.7%
9.84 1
 
0.7%
9.62 1
 
0.7%
9.26 1
 
0.7%
8.53 1
 
0.7%
8.14 1
 
0.7%
8.12 1
 
0.7%
8.03 1
 
0.7%
7.82 1
 
0.7%
Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
0.9881000000000001
71 
1.0
68 

Length

Max length18
Median length18
Mean length10.661871
Min length3

Characters and Unicode

Total characters1482
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.9881000000000001
2nd row0.9881000000000001
3rd row0.9881000000000001
4th row0.9881000000000001
5th row0.9881000000000001

Common Values

ValueCountFrequency (%)
0.9881000000000001 71
51.1%
1.0 68
48.9%

Length

2022-12-08T19:53:30.580443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:30.685858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.9881000000000001 71
51.1%
1.0 68
48.9%

Most occurring characters

ValueCountFrequency (%)
0 920
62.1%
1 210
 
14.2%
8 142
 
9.6%
. 139
 
9.4%
9 71
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1343
90.6%
Other Punctuation 139
 
9.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 920
68.5%
1 210
 
15.6%
8 142
 
10.6%
9 71
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1482
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 920
62.1%
1 210
 
14.2%
8 142
 
9.6%
. 139
 
9.4%
9 71
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1482
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 920
62.1%
1 210
 
14.2%
8 142
 
9.6%
. 139
 
9.4%
9 71
 
4.8%
Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Não
139 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNão
2nd rowNão
3rd rowNão
4th rowNão
5th rowNão

Common Values

ValueCountFrequency (%)
Não 139
100.0%

Length

2022-12-08T19:53:30.766186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:30.868121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
não 139
100.0%

Most occurring characters

ValueCountFrequency (%)
N 139
33.3%
ã 139
33.3%
o 139
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 278
66.7%
Uppercase Letter 139
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
ã 139
50.0%
o 139
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 417
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 139
33.3%
ã 139
33.3%
o 139
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 278
66.7%
None 139
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 139
50.0%
o 139
50.0%
None
ValueCountFrequency (%)
ã 139
100.0%
Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Firjan/2019
139 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1529
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirjan/2019
2nd rowFirjan/2019
3rd rowFirjan/2019
4th rowFirjan/2019
5th rowFirjan/2019

Common Values

ValueCountFrequency (%)
Firjan/2019 139
100.0%

Length

2022-12-08T19:53:30.947938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-08T19:53:31.047231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
firjan/2019 139
100.0%

Most occurring characters

ValueCountFrequency (%)
F 139
9.1%
i 139
9.1%
r 139
9.1%
j 139
9.1%
a 139
9.1%
n 139
9.1%
/ 139
9.1%
2 139
9.1%
0 139
9.1%
1 139
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 695
45.5%
Decimal Number 556
36.4%
Uppercase Letter 139
 
9.1%
Other Punctuation 139
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 139
20.0%
r 139
20.0%
j 139
20.0%
a 139
20.0%
n 139
20.0%
Decimal Number
ValueCountFrequency (%)
2 139
25.0%
0 139
25.0%
1 139
25.0%
9 139
25.0%
Uppercase Letter
ValueCountFrequency (%)
F 139
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 834
54.5%
Common 695
45.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 139
16.7%
i 139
16.7%
r 139
16.7%
j 139
16.7%
a 139
16.7%
n 139
16.7%
Common
ValueCountFrequency (%)
/ 139
20.0%
2 139
20.0%
0 139
20.0%
1 139
20.0%
9 139
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1529
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 139
9.1%
i 139
9.1%
r 139
9.1%
j 139
9.1%
a 139
9.1%
n 139
9.1%
/ 139
9.1%
2 139
9.1%
0 139
9.1%
1 139
9.1%

Finanças - Fiscal (Indicador)
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct123
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4782662
Minimum0
Maximum10
Zeros13
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-12-08T19:53:31.143370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.5605
median4.668
Q35.778
95-th percentile7.513
Maximum10
Range10
Interquartile range (IQR)2.2175

Descriptive statistics

Standard deviation2.0834301
Coefficient of variation (CV)0.4652314
Kurtosis0.3038534
Mean4.4782662
Median Absolute Deviation (MAD)1.13
Skewness-0.44779564
Sum622.479
Variance4.3406808
MonotonicityNot monotonic
2022-12-08T19:53:31.265076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
9.4%
4.878 2
 
1.4%
3.965 2
 
1.4%
5.045 2
 
1.4%
3.088 2
 
1.4%
4.043 1
 
0.7%
5.535 1
 
0.7%
5.533 1
 
0.7%
6.065 1
 
0.7%
4.838 1
 
0.7%
Other values (113) 113
81.3%
ValueCountFrequency (%)
0 13
9.4%
1.24 1
 
0.7%
1.53 1
 
0.7%
2.28 1
 
0.7%
2.368 1
 
0.7%
2.418 1
 
0.7%
2.523 1
 
0.7%
2.55 1
 
0.7%
2.57 1
 
0.7%
2.6 1
 
0.7%
ValueCountFrequency (%)
10 1
0.7%
8.843 1
0.7%
8.635 1
0.7%
8.213 1
0.7%
7.78 1
0.7%
7.653 1
0.7%
7.63 1
0.7%
7.5 1
0.7%
7.375 1
0.7%
7.22 1
0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB

Interactions

2022-12-08T19:53:19.473804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:49.133690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:50.807335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:52.445486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:54.267907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:56.351844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:58.178985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:59.917243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:01.632780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:03.523759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:05.219934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:06.868014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:08.838122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:10.609165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:12.310888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:14.080136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:15.762659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:17.562304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:19.566043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:49.236335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:50.903946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:52.537886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:54.356940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:56.457016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:58.266273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:00.012912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:01.729936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:03.617986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:05.303519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:06.957995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:08.926715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:10.691732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:12.399969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:14.163920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:15.860030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:17.651296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:19.659801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:49.329431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:50.988798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:52.628892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:54.446298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:56.555415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:58.359267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:00.102123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:01.825694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:03.707157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:05.389985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:07.057294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:09.016047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:10.781895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:12.488034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:14.250629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:15.955644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:17.737715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:19.765991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:49.422013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:51.089222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:52.731932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:54.553211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:56.674763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:58.457315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:00.198569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:01.939821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:03.806862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:05.487135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:07.424747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:09.115945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:10.889798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:12.594491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:14.344344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:16.067228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:17.836854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:19.859748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:49.524489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:51.186180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:52.835114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:54.661799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:56.787155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:58.562963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:00.297983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:02.062382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:03.909131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:05.583051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:07.527995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:09.228353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:11.000227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:12.704139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:14.443257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:16.171437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:17.931787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:19.955928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:49.631871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:51.279238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-08T19:52:56.119574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:57.992958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:59.738175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:01.452372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:03.320350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:05.038968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:06.691942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:08.653830image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:10.425173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:12.133931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:13.885173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:15.590883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:17.372363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:19.305841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:21.064996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:50.720635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:52.353717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:54.171682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:56.222011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:58.084208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:52:59.828987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:01.541010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:03.423897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:05.129201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:06.780137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:08.748173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:10.521056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:12.223013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:13.976077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:15.676607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:17.465114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-08T19:53:19.389095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-08T19:53:31.389865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-08T19:53:31.745201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-08T19:53:32.063593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-08T19:53:32.373993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-08T19:53:32.667355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-08T19:53:32.841755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-08T19:53:21.285418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-08T19:53:22.078773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

anocodigo_municipioCódigo IBGEnomeestado (sigla)RegiãoNome_UFcapital (s/n)ClusterDados de Identificação/Demográficos - PopulaçãoDados de Identificação/Demográficos - População - FonteÁrea da Unidade TerritorialÁrea da Unidade Territorial - FontePib per capita 2020Pib per capita 2020 - FontePib a preços correntes (R$ 1.000)Pib a preços correntes (R$ 1.000) - FonteFinanças - Investimento per Capita - Invest. Educação - Dado BrutoFinanças - Investimento per Capita - Invest. Educação - NotaFinanças - Investimento per Capita - Invest. Educação - MetaFinanças - Investimento per Capita - Invest. Educação - OutlierFinanças - Investimento per Capita - Invest. Educação - FonteFinanças - Investimento per Capita - Invest. Saúde - Dado BrutoFinanças - Investimento per Capita - Invest. Saúde - NotaFinanças - Investimento per Capita - Invest. Saúde - MetaFinanças - Investimento per Capita - Invest. Saúde - OutlierFinanças - Investimento per Capita - Invest. Saúde - FonteFinanças - Investimento per Capita (Indicador)Finanças - Fiscal - Autonomia - Dado BrutoFinanças - Fiscal - Autonomia - NotaFinanças - Fiscal - Autonomia - MetaFinanças - Fiscal - Autonomia - OutlierFinanças - Fiscal - Autonomia - FonteFinanças - Fiscal - Gasto com Pessoal - Dado BrutoFinanças - Fiscal - Gasto com Pessoal - NotaFinanças - Fiscal - Gasto com Pessoal - MetaFinanças - Fiscal - Gasto com Pessoal - OutlierFinanças - Fiscal - Gasto com Pessoal - FonteFinanças - Fiscal - Investimentos - Dado BrutoFinanças - Fiscal - Investimentos - NotaFinanças - Fiscal - Investimentos - MetaFinanças - Fiscal - Investimentos - OutlierFinanças - Fiscal - Investimentos - FonteFinanças - Fiscal - Liquidez - Dado BrutoFinanças - Fiscal - Liquidez - NotaFinanças - Fiscal - Liquidez - MetaFinanças - Fiscal - Liquidez - OutlierFinanças - Fiscal - Liquidez - FonteFinanças - Fiscal (Indicador)Finanças - Equilíbrio Previdenciário - Indicador Situação Prev. - Dado Bruto
2715420211715751715754PALMEIROPOLISTONortePALMEIROPOLIS - TOnGrupo 17676IBGE/20201703.94IBGE/201015543.80IBGE/2020119314.20880IBGE/2020948.0747755.661354.959465NãoSTN/2019758.9711376.44980.53246NãoSTN/20196.0500.0842.660.3161NãoFirjan/20190.5166.600.7822NãoFirjan/2019110.000.914NãoFirjan/20190.7477.560.9881NãoFirjan/20196.7054.5
2715520211720901720903TAGUATINGATONorteTAGUATINGA - TOnGrupo 116825IBGE/20202437.40IBGE/201014648.47IBGE/2020246460.50775IBGE/2020635.4340022.331354.959465NãoSTN/2019718.2641285.79980.53246NãoSTN/20194.0600.33410.000.3161NãoFirjan/201900.000.7822NãoFirjan/20190.192.080.914NãoFirjan/2019110.000.9881NãoFirjan/20195.5204.25
2715620211707201707207DOIS IRMAOS DO TOCANTINSTONorteDOIS IRMAOS DO TOCANTINS - TOnGrupo 17185IBGE/20203757.04IBGE/201014213.92IBGE/2020102127.01520IBGE/2020761.1447533.671354.959465NãoSTN/2019660.482754.86980.53246NãoSTN/20194.2650.1414.460.3161NãoFirjan/20190.83910.000.7822NãoFirjan/20190.3363.680.914NãoFirjan/2019110.000.9881NãoFirjan/20197.0354.5
2715720211702551702554AUGUSTINOPOLISTONorteAUGUSTINOPOLIS - TOnGrupo 118643IBGE/2020394.98IBGE/201013961.17IBGE/2020260278.09231IBGE/2020778.2691933.851354.959465NãoSTN/2019466.7930641.75980.53246NãoSTN/20192.8000.1043.290.3161NãoFirjan/20190.2433.110.7822NãoFirjan/20190.6316.900.914NãoFirjan/20190.4834.890.9881NãoFirjan/20194.548nd
2715820211703801703800BURITI DO TOCANTINSTONorteBURITI DO TOCANTINS - TOnGrupo 111497IBGE/2020251.92IBGE/20108731.58IBGE/2020100386.97526IBGE/2020700.9622873.031354.959465NãoSTN/2019449.2876521.47980.53246NãoSTN/20192.25000.000.3161NãoFirjan/20190.6698.550.7822NãoFirjan/20190.6797.430.914NãoFirjan/20190.7447.530.9881NãoFirjan/20195.878nd
2715920211720201720200SAO MIGUEL DO TOCANTINSTONorteSAO MIGUEL DO TOCANTINS - TOnGrupo 112294IBGE/2020398.82IBGE/20108836.50IBGE/2020108635.93100IBGE/20201073.2481137.001354.959465NãoSTN/2019531.0388352.78980.53246NãoSTN/20194.89000.000.3161NãoFirjan/201900.000.7822NãoFirjan/20190.4114.500.914NãoFirjan/20190.4914.970.9881NãoFirjan/20192.368nd
2716020211717901717909PONTE ALTA DO TOCANTINSTONortePONTE ALTA DO TOCANTINS - TOnGrupo 18116IBGE/20206491.13IBGE/201010574.22IBGE/202085820.36952IBGE/2020709.4653893.121354.959465NãoSTN/2019521.6437852.63980.53246NãoSTN/20192.87500.000.3161NãoFirjan/20190.2032.600.7822NãoFirjan/20190.1791.960.914NãoFirjan/20190.6026.090.9881NãoFirjan/20192.6633
2716120211720801720804SITIO NOVO DO TOCANTINSTONorteSITIO NOVO DO TOCANTINS - TOnGrupo 18997IBGE/2020324.10IBGE/201011252.11IBGE/2020101235.23367IBGE/20201425.70295110.001354.959465NãoSTN/2019765.6722116.55980.53246NãoSTN/20198.27500.000.3161NãoFirjan/201900.000.7822NãoFirjan/20190.8128.880.914NãoFirjan/20190.5475.540.9881NãoFirjan/20193.605nd
2716220211710501710508ITACAJATONorteITACAJA - TOnGrupo 17452IBGE/20203051.36IBGE/201013414.29IBGE/202099963.28908IBGE/2020748.384913.541354.959465NãoSTN/2019623.4328764.27980.53246NãoSTN/20193.9050.56910.000.3161NãoFirjan/20190.11.280.7822NãoFirjan/20190.6847.480.914NãoFirjan/20190.4474.520.9881NãoFirjan/20195.820nd
2716320211713801713809PALMEIRAS DO TOCANTINSTONortePALMEIRAS DO TOCANTINS - TOnGrupo 16745IBGE/2020747.90IBGE/201010464.86IBGE/202070585.48070IBGE/2020766.5798673.731354.959465NãoSTN/2019596.087153.83980.53246NãoSTN/20193.78000.000.3161NãoFirjan/20190.7429.490.7822NãoFirjan/20190.8359.140.914NãoFirjan/20190.4934.990.9881NãoFirjan/20195.905nd
anocodigo_municipioCódigo IBGEnomeestado (sigla)RegiãoNome_UFcapital (s/n)ClusterDados de Identificação/Demográficos - PopulaçãoDados de Identificação/Demográficos - População - FonteÁrea da Unidade TerritorialÁrea da Unidade Territorial - FontePib per capita 2020Pib per capita 2020 - FontePib a preços correntes (R$ 1.000)Pib a preços correntes (R$ 1.000) - FonteFinanças - Investimento per Capita - Invest. Educação - Dado BrutoFinanças - Investimento per Capita - Invest. Educação - NotaFinanças - Investimento per Capita - Invest. Educação - MetaFinanças - Investimento per Capita - Invest. Educação - OutlierFinanças - Investimento per Capita - Invest. Educação - FonteFinanças - Investimento per Capita - Invest. Saúde - Dado BrutoFinanças - Investimento per Capita - Invest. Saúde - NotaFinanças - Investimento per Capita - Invest. Saúde - MetaFinanças - Investimento per Capita - Invest. Saúde - OutlierFinanças - Investimento per Capita - Invest. Saúde - FonteFinanças - Investimento per Capita (Indicador)Finanças - Fiscal - Autonomia - Dado BrutoFinanças - Fiscal - Autonomia - NotaFinanças - Fiscal - Autonomia - MetaFinanças - Fiscal - Autonomia - OutlierFinanças - Fiscal - Autonomia - FonteFinanças - Fiscal - Gasto com Pessoal - Dado BrutoFinanças - Fiscal - Gasto com Pessoal - NotaFinanças - Fiscal - Gasto com Pessoal - MetaFinanças - Fiscal - Gasto com Pessoal - OutlierFinanças - Fiscal - Gasto com Pessoal - FonteFinanças - Fiscal - Investimentos - Dado BrutoFinanças - Fiscal - Investimentos - NotaFinanças - Fiscal - Investimentos - MetaFinanças - Fiscal - Investimentos - OutlierFinanças - Fiscal - Investimentos - FonteFinanças - Fiscal - Liquidez - Dado BrutoFinanças - Fiscal - Liquidez - NotaFinanças - Fiscal - Liquidez - MetaFinanças - Fiscal - Liquidez - OutlierFinanças - Fiscal - Liquidez - FonteFinanças - Fiscal (Indicador)Finanças - Equilíbrio Previdenciário - Indicador Situação Prev. - Dado Bruto
2728320211702201702208ARAGUATINSTONorteARAGUATINS - TOnGrupo 336170IBGE/20202625.29IBGE/201012060.54IBGE/20204.362297e+05IBGE/2020674.7786722.931248.735078NãoSTN/2019478.2066294.39673.546441NãoSTN/20193.66000.000.338NãoFirjan/201900.000.7195NãoFirjan/20190.3594.440.7930NãoFirjan/2019110.001.0NãoFirjan/20193.6104.75
2728420211721201721208TOCANTINOPOLISTONorteTOCANTINOPOLIS - TOnGrupo 322845IBGE/20201077.07IBGE/201012913.56IBGE/20202.950103e+05IBGE/2020712.1522573.391248.735078NãoSTN/2019716.47933510.00673.546441NãoSTN/20196.6950.47210.000.338NãoFirjan/20190.2713.770.7195NãoFirjan/20190.9510.000.7930NãoFirjan/20190.5735.731.0NãoFirjan/20197.375nd
2728520211707001707009DIANOPOLISTONorteDIANOPOLIS - TOnGrupo 322424IBGE/20203217.31IBGE/201015912.16IBGE/20203.568143e+05IBGE/2020761.5699134.001248.735078NãoSTN/2019464.6942624.00673.546441NãoSTN/20194.0000.51910.000.338NãoFirjan/201900.000.7195NãoFirjan/20190.1051.180.7930NãoFirjan/2019110.001.0NãoFirjan/20195.2955.25
2728620211709301709302GUARAITONorteGUARAI - TOnGrupo 426165IBGE/20202268.16IBGE/201025371.81IBGE/20206.638534e+05IBGE/2020643.5503562.621170.632635NãoSTN/2019531.5789851.90991.025958NãoSTN/20192.2600.9149.141.000NãoFirjan/20190.3293.510.9366NãoFirjan/20190.618.340.7296NãoFirjan/20190.9629.621.0NãoFirjan/20197.6534.5
2728720211705501705508COLINAS DO TOCANTINSTONorteCOLINAS DO TOCANTINS - TOnGrupo 435851IBGE/2020843.85IBGE/201020752.91IBGE/20207.440126e+05IBGE/2020876.1456835.871170.632635NãoSTN/2019650.7482634.00991.025958NãoSTN/20194.9350.4634.631.000NãoFirjan/20190.0210.220.9366NãoFirjan/20190.3444.660.7296NãoFirjan/2019110.001.0NãoFirjan/20194.8785.5
2728820211716101716109PARAISO DO TOCANTINSTONortePARAISO DO TOCANTINS - TOnGrupo 651891IBGE/20201268.06IBGE/201024316.23IBGE/20201.261793e+06IBGE/2020586.2883812.421246.871303NãoSTN/2019498.9111.361075.024453NãoSTN/20191.8900.8398.271.000NãoFirjan/20190.474.701.0000NãoFirjan/20190.5828.470.6760NãoFirjan/20190.6696.691.0NãoFirjan/20197.0335.5
2728920211709501709500GURUPITONorteGURUPI - TOnGrupo 687545IBGE/20201836.09IBGE/201025690.42IBGE/20202.249068e+06IBGE/20201432.11455610.001246.871303NãoSTN/2019585.2083412.651075.024453NãoSTN/20196.325110.001.000NãoFirjan/20190.2982.981.0000NãoFirjan/20190.5257.540.6760NãoFirjan/2019110.001.0NãoFirjan/20197.6304
2729020211718201718204PORTO NACIONALTONortePORTO NACIONAL - TOnGrupo 653316IBGE/20204449.92IBGE/201031830.46IBGE/20201.697073e+06IBGE/2020898.8129956.011246.871303NãoSTN/2019701.7069174.401075.024453NãoSTN/20195.2050.5645.301.000NãoFirjan/20190.3653.651.0000NãoFirjan/20190.4346.070.6760NãoFirjan/2019110.001.0NãoFirjan/20196.2555
2729120211702101702109ARAGUAINATONorteARAGUAINA - TOnGrupo 7183381IBGE/20204000.42IBGE/201024715.96IBGE/20204.532437e+06IBGE/2020873.0844699.86881.888682NãoSTN/2019591.0602325.59859.485444NãoSTN/20197.7250.656.501.000NãoFirjan/20190.7277.660.9488NãoFirjan/20190.3254.720.6558NãoFirjan/2019110.001.0NãoFirjan/20197.2204
2729220211721001721000PALMASTONortePALMAS - TOsGrupo 8306296IBGE/20202218.94IBGE/201032293.89IBGE/20209.891489e+06IBGE/20201010.0558648.391094.205857NãoSTN/2019738.1853433.421264.808039NãoSTN/20195.9050.8868.861.000NãoFirjan/20190.6826.821.0000NãoFirjan/20190.5729.040.6320NãoFirjan/20190.646.401.0NãoFirjan/20197.7803.75